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Task 3 (Hard): Performance & Correctness Review of a Distributed LRU Cache.
The agent reviews a Python LRU cache with Redis backing containing:
1. Race condition (non-atomic check-then-act on Redis)
2. Memory leak (unbounded local dict grows forever)
3. N+1 query pattern (per-key pipeline not batched)
4. Incorrect LRU eviction (uses insertion order, not access order)
5. Thread-safety violation (shared dict without lock)
6. Silent data corruption (pickle loads untrusted bytes)
"""
from __future__ import annotations
from typing import Any, Dict
TASK_ID = "task_3_hard_perf_correctness"
MAX_STEPS = 16
BUGGY_CODE = '''\
import pickle
import threading
import redis
class DistributedLRUCache:
"""
LRU cache backed by Redis for distributed deployments.
Local dict acts as an L1 write-through layer.
"""
def __init__(self, capacity: int, redis_url: str = "redis://localhost:6379"):
self.capacity = capacity
self.local = {} # ISSUE 2 & 5: shared dict, no lock, unbounded growth
self.redis = redis.from_url(redis_url)
self.hits = 0
self.misses = 0
# ── ISSUE 5: no lock; concurrent writes race on self.local ──────────────
def get(self, key: str):
if key in self.local:
self.hits += 1
return self.local[key] # ISSUE 4: doesn't update LRU order
# ISSUE 1: race condition — between EXISTS and GET another process may delete key
if self.redis.exists(key):
raw = self.redis.get(key)
value = pickle.loads(raw) # ISSUE 6: deserialising untrusted bytes
self.local[key] = value # ISSUE 2: local dict grows without bound
self.hits += 1
return value
self.misses += 1
return None
def put(self, key: str, value, ttl: int = 300):
# ISSUE 2: no eviction from self.local; grows forever
self.local[key] = value
# ISSUE 1: non-atomic: set + expire are two separate commands
self.redis.set(key, pickle.dumps(value))
self.redis.expire(key, ttl)
def get_many(self, keys: list):
# ISSUE 3: N+1 — calls self.get() in a loop instead of using pipeline/mget
return {k: self.get(k) for k in keys}
def invalidate(self, key: str):
self.local.pop(key, None)
self.redis.delete(key)
def stats(self):
total = self.hits + self.misses
return {
"hits": self.hits,
"misses": self.misses,
"hit_rate": self.hits / total if total else 0,
"local_size": len(self.local),
}
'''
FIXED_CODE = '''\
import json
import threading
from collections import OrderedDict
import redis
class DistributedLRUCache:
"""
Thread-safe LRU cache backed by Redis.
Uses OrderedDict for correct LRU eviction, a Lock for thread safety,
atomic Redis SET EX commands, and mget for batch fetching.
Serialises with JSON (not pickle) to avoid arbitrary code execution.
"""
def __init__(self, capacity: int, redis_url: str = "redis://localhost:6379"):
self.capacity = capacity
self.local: OrderedDict = OrderedDict() # correct LRU order
self._lock = threading.Lock() # thread safety
self.redis = redis.from_url(redis_url)
self.hits = 0
self.misses = 0
def get(self, key: str):
with self._lock:
if key in self.local:
self.local.move_to_end(key) # update LRU order
self.hits += 1
return self.local[key]
raw = self.redis.get(key) # atomic single GET, no race
if raw is not None:
value = json.loads(raw) # safe deserialisation
with self._lock:
self._evict_if_needed()
self.local[key] = value
self.hits += 1
return value
with self._lock:
self.misses += 1
return None
def _evict_if_needed(self):
"""Call with self._lock held."""
while len(self.local) >= self.capacity:
self.local.popitem(last=False) # evict LRU item
def put(self, key: str, value, ttl: int = 300):
payload = json.dumps(value)
self.redis.set(key, payload, ex=ttl) # atomic SET with TTL
with self._lock:
self.local[key] = value
self.local.move_to_end(key)
self._evict_if_needed()
def get_many(self, keys: list):
"""Batch fetch using Redis MGET — O(1) round trips."""
if not keys:
return {}
raws = self.redis.mget(keys)
result = {}
with self._lock:
for key, raw in zip(keys, raws):
if raw is not None:
value = json.loads(raw)
self._evict_if_needed()
self.local[key] = value
self.hits += 1
result[key] = value
else:
self.misses += 1
result[key] = None
return result
def invalidate(self, key: str):
with self._lock:
self.local.pop(key, None)
self.redis.delete(key)
def stats(self):
with self._lock:
total = self.hits + self.misses
return {
"hits": self.hits,
"misses": self.misses,
"hit_rate": self.hits / total if total else 0,
"local_size": len(self.local),
}
'''
KNOWN_ISSUES = {
"race_condition": {
"lines": [23, 43],
"description_keywords": ["race condition", "atomic", "exists", "set", "pipeline", "non-atomic"],
"severity": "critical",
"issue_type": "concurrency",
},
"memory_leak": {
"lines": [13, 27, 38],
"description_keywords": ["memory leak", "unbounded", "evict", "capacity", "grow"],
"severity": "critical",
"issue_type": "performance",
},
"n_plus_one": {
"lines": [47],
"description_keywords": ["n+1", "pipeline", "mget", "batch", "loop", "round trip"],
"severity": "major",
"issue_type": "performance",
},
"wrong_lru_order": {
"lines": [21, 24],
"description_keywords": ["lru", "order", "move_to_end", "access order", "insertion order", "OrderedDict"],
"severity": "major",
"issue_type": "logic",
},
"thread_safety": {
"lines": [13],
"description_keywords": ["thread", "lock", "concurrent", "race", "mutex", "atomic"],
"severity": "critical",
"issue_type": "concurrency",
},
"pickle_injection": {
"lines": [26],
"description_keywords": ["pickle", "deseri", "arbitrary code", "injection", "untrusted", "json"],
"severity": "critical",
"issue_type": "security",
},
}
PULL_REQUEST = {
"pull_request_title": "Introduce DistributedLRUCache with Redis backing for session store",
"author": "senior-eng",
"description": (
"Implements a two-tier LRU cache (local + Redis) to reduce DB load by 60%. "
"Designed for high-throughput production use. Please review thoroughly."
),
"files_changed": [
{
"filename": "cache.py",
"language": "python",
"content": BUGGY_CODE,
"line_count": BUGGY_CODE.count("\n") + 1,
}
],
"test_results": "Unit tests pass. Load tests not yet run.",
"linter_output": "No issues found by flake8.",
}
def get_task_config() -> Dict[str, Any]:
return {
"task_id": TASK_ID,
"max_steps": MAX_STEPS,
"pull_request": PULL_REQUEST,
"known_issues": KNOWN_ISSUES,
"fixed_code": FIXED_CODE,
"difficulty": "hard",
"description": (
"Review a production-grade distributed LRU cache implementation. "
"Identify all concurrency, performance, correctness, and security issues. "
"Provide a fully corrected implementation."
),
}
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